SaoMNK Tutorial: Decreasing density transitions
S. Downing
2025-01-29
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############### Load R6 Class DEPENDENCIES ############################
## Biparite Environment Search Simulation Class
SaomNkRSienaBiEnv <- source(file.path(dir_r, 'SAOM_NK_R6_model.R'))$value
# ## RSiena search Class
# SaomNkRSienaBiEnv_search_rsiena <- source(file.path(dir_proj, 'SAOM_NK_R6_search_rsiena_model.R'))$value
## default settings: Users do not change; TODO: implment within restricted class attributes
DV_NAME <- 'self$bipartite_rsienaDV'1. NO Endogenous Noise in Environment (Structural Effects = 0)
1.1 Environment Configuration
#
environ_params <- list(
M = 12, ## Actors
N = 6, ## Components
BI_PROB = 0, ## Environmental Density (DGP hyperparameter)
component_matrix_start = 'rand', ##**TODO** Implement: 'rand','modular','semi-modular',...
rand_seed = 1234,
plot_init = F,
name = '_test_tutorial_nb_'
)
#
env1 <- SaomNkRSienaBiEnv$new(environ_params)##
## TEST FROM CALLED CLASS INIT *BEFORE* BASE INIT
##
## CALLED _BASE_ INIT
##
## TEST FROM CALLED CLASS INIT *AFTER* BASE INIT1.2 Structrual Model
SAOM Objective Function serves as the stochastic actor’s utility function for strategic search.
#
strategies <- list(
egoX = c(-1, 0, 1),
inPopX = c( 1, 0, -1)
)
## 2.b. Component Payoffs vector
set.seed(12345)
component_payoffs <- runif(environ_params$N, min = 0, max = 1)
## 2. Strategies sets the objective function as a linear combination of network stats across DVs
#
actor_strats_list <- lapply(strategies, function(strat) rep(strat, environ_params$M/length(strat)) )
#
structure_model <- list(
dv_bipartite = list(
name = 'self$bipartite_rsienaDV',
effects = list( ##**STRUCTURAL EFFECTS -- dyadic/network endogeneity sources**
list(effect='density', parameter= 0, fix=T, dv_name=DV_NAME), ##interaction1 = NULL
list(effect='inPop', parameter= 0, fix=T, dv_name=DV_NAME), #interaction1 = NUL
list(effect='outAct', parameter= 0, fix=T, dv_name=DV_NAME)
),
## COVARIATE EFFECTS
coCovars = list(
##** COMPONENTS : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='altX', parameter= 1, dv_name=DV_NAME, fix=T,
interaction1='self$component_1_coCovar', x = component_payoffs
),
##** STRATEGIES : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='egoX', parameter= .1, dv_name=DV_NAME, fix=T,
interaction1='self$strat_1_coCovar', x = actor_strats_list[[1]]
), #interaction1 = NULL
list(effect='inPopX', parameter= .1, dv_name=DV_NAME, fix=T,
interaction1='self$strat_2_coCovar', x = actor_strats_list[[2]]
)
),
varCovars = list() ##**MONADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC STRATEGY PROGRAMS**
)
)env1$preview_effects(structure_model, filter=FALSE)## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html.html .env1$preview_effects(structure_model, filter=TRUE)## Effects documentation written to file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_rsiena_effects_doc_.html.html .1.3 Run RSiena Search Process
## Run Rsiena search using variable parameters in theta_matrix
env1$search_rsiena(
structure_model,
iterations = env1$M * 4,
digits = 4,
run_seed = 12345
)##
##
## theta_matrix :
##
## density inPop outAct altX egoX inPopX
## [1,] 0 0 0 1 0.1 0.1
## [2,] 0 0 0 1 0.1 0.1
## [3,] 0 0 0 1 0.1 0.1
## [4,] 0 0 0 1 0.1 0.1
## [5,] 0 0 0 1 0.1 0.1
## [6,] 0 0 0 1 0.1 0.1
## [7,] 0 0 0 1 0.1 0.1
## [8,] 0 0 0 1 0.1 0.1
## [9,] 0 0 0 1 0.1 0.1
## [10,] 0 0 0 1 0.1 0.1
## [11,] 0 0 0 1 0.1 0.1
## [12,] 0 0 0 1 0.1 0.1
## [13,] 0 0 0 1 0.1 0.1
## [14,] 0 0 0 1 0.1 0.1
## [15,] 0 0 0 1 0.1 0.1
## [16,] 0 0 0 1 0.1 0.1
## [17,] 0 0 0 1 0.1 0.1
## [18,] 0 0 0 1 0.1 0.1
## [19,] 0 0 0 1 0.1 0.1
## [20,] 0 0 0 1 0.1 0.1
## [21,] 0 0 0 1 0.1 0.1
## [22,] 0 0 0 1 0.1 0.1
## [23,] 0 0 0 1 0.1 0.1
## [24,] 0 0 0 1 0.1 0.1
## [25,] 0 0 0 1 0.1 0.1
## [26,] 0 0 0 1 0.1 0.1
## [27,] 0 0 0 1 0.1 0.1
## [28,] 0 0 0 1 0.1 0.1
## [29,] 0 0 0 1 0.1 0.1
## [30,] 0 0 0 1 0.1 0.1
## [31,] 0 0 0 1 0.1 0.1
## [32,] 0 0 0 1 0.1 0.1
## [33,] 0 0 0 1 0.1 0.1
## [34,] 0 0 0 1 0.1 0.1
## [35,] 0 0 0 1 0.1 0.1
## [36,] 0 0 0 1 0.1 0.1
## [37,] 0 0 0 1 0.1 0.1
## [38,] 0 0 0 1 0.1 0.1
## [39,] 0 0 0 1 0.1 0.1
## [40,] 0 0 0 1 0.1 0.1
## [41,] 0 0 0 1 0.1 0.1
## [42,] 0 0 0 1 0.1 0.1
## [43,] 0 0 0 1 0.1 0.1
## [44,] 0 0 0 1 0.1 0.1
## [45,] 0 0 0 1 0.1 0.1
## [46,] 0 0 0 1 0.1 0.1
## [47,] 0 0 0 1 0.1 0.1
## [48,] 0 0 0 1 0.1 0.1
##
##
## self$rsiena_data :
##
## Dependent variables: self$bipartite_rsienaDV
## Number of observations: 2
##
## Nodesets ACTORS COMPONENTS
## Number of nodes 12 6
##
## Dependent variable self$bipartite_rsienaDV
## Type bipartite
## Observations 2
## First nodeset ACTORS
## Second nodeset COMPONENTS
## Densities NA NA
##
## Constant covariates: self$component_1_coCovar, self$strat_1_coCovar, self$strat_2_coCovar
##
## structural effects i=1, j=1
## $effect
## [1] "density"
##
## $parameter
## [1] 0
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE -1.60944 0
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=2
## $effect
## [1] "inPop"
##
## $parameter
## [1] 0
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
##
## structural effects i=1, j=3
## $effect
## [1] "outAct"
##
## $parameter
## [1] 0
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
##
## coCovars i=1, j=1
## $effect
## [1] "altX"
##
## $parameter
## [1] 1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$component_1_coCovar"
##
## $x
## [1] 0.7209039 0.8757732 0.7609823 0.8861246 0.4564810 0.1663718
##
## effectName include fix test initialValue parm
## 1 self$component_1_coCovar alter TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$component_1_coCovar alter TRUE TRUE FALSE 0 1
##
## coCovars i=1, j=2
## $effect
## [1] "egoX"
##
## $parameter
## [1] 0.1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $x
## [1] -1 0 1 -1 0 1 -1 0 1 -1 0 1
##
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0.1
##
## coCovars i=1, j=3
## $effect
## [1] "inPopX"
##
## $parameter
## [1] 0.1
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $fix
## [1] TRUE
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $x
## [1] 1 0 -1 1 0 -1 1 0 -1 1 0 -1
##
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 1
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 0.1
## If you use this algorithm object, siena07 will create/use an output file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_test_tutorial_nb__173813296720.txt .
##
## Start phase 0
## theta: 0 0 0 0 0 0
##
## Start phase 3
## Parameter values used for simulations
##
## Mean Standard
## value Deviation
##
## Rate parameters:
## 0 Rate parameter NA ( NA )
##
## Other parameters:
## 1. eval outdegree (density) 0.0 ( 0 )
## 2. eval indegree - popularity 0.0 ( 0 )
## 3. eval outdegree - activity 0.0 ( 0 )
## 4. eval self$component_1_coCovar alter 1.0 ( 0 )
## 5. eval self$strat_1_coCovar ego 0.1 ( 0 )
## 6. eval ind. pop.^(1/0.1) weighted self$strat_2_coCovar 0.1 ( 0 )
##
## Simulated means and standard deviations
## 1. Number of ties 0.792 0.410
## 2. Sum of squared indegrees 0.792 0.410
## 3. Sum of squared outdegrees 0.792 0.410
## 4. Sum of indegrees x self$component_1_coCovar 0.019 0.208
## 5. Sum of outdegrees x self$strat_1_coCovar -0.042 0.798
## 6. indegree pop.^(1/0.1) weighted self$strat_2_coCovar 0.042 0.798
##
##
## Simulated statistics are in x$sf
## and simulated dependent variables in x$sims, where x is the created object.
##
## Total of 48 iteration steps.
##
## Covariance matrix of estimates (correlations below diagonal)
##
## 0 0 0 0 0 0
## NaN 0 0 0 0 0
## NaN NaN 0 0 0 0
## NaN NaN NaN 0 0 0
## NaN NaN NaN NaN 0 0
## NaN NaN NaN NaN NaN 0
##
## Derivative matrix of expected statistics X by parameters:
##
## 0.165 0.165 0.165 0.004 -0.016 0.016
## 0.165 0.165 0.165 0.004 -0.016 0.016
## 0.165 0.165 0.165 0.004 -0.016 0.016
## 0.004 0.004 0.004 0.042 -0.022 0.022
## -0.002 -0.002 -0.002 -0.003 0.087 -0.087
## 0.000 0.000 0.000 0.000 0.000 0.000
##
## Covariance matrix of X (correlations below diagonal):
##
## 0.168 0.168 0.168 0.004 -0.009 0.009
## 1.000 0.168 0.168 0.004 -0.009 0.009
## 1.000 1.000 0.168 0.004 -0.009 0.009
## 0.048 0.048 0.048 0.043 -0.022 0.022
## -0.027 -0.027 -0.027 -0.131 0.637 -0.637
## 0.027 0.027 0.027 0.131 -1.000 0.637
##
##
##
## Simulated Decision Chain Summary:
##
## dv_type dv_type_bin dv_varname id_from
## Length:48 Min. :0 Length:48 Min. : 1.000
## Class :character 1st Qu.:0 Class :character 1st Qu.: 4.000
## Mode :character Median :0 Mode :character Median : 7.000
## Mean :0 Mean : 6.708
## 3rd Qu.:0 3rd Qu.: 9.250
## Max. :0 Max. :12.000
## id_to beh_difference reciprocal_rate LogOptionSetProb
## Min. :1.000 Min. :0 Min. :0.08333 Min. :-2.485
## 1st Qu.:2.000 1st Qu.:0 1st Qu.:0.08333 1st Qu.:-2.485
## Median :4.000 Median :0 Median :0.08333 Median :-2.485
## Mean :4.146 Mean :0 Mean :0.08333 Mean :-2.485
## 3rd Qu.:6.000 3rd Qu.:0 3rd Qu.:0.08333 3rd Qu.:-2.485
## Max. :7.000 Max. :0 Max. :0.08333 Max. :-2.485
## LogChoiceProb diagonal stability tie_change
## Min. :-2.465 Length:48 Mode :logical Mode :logical
## 1st Qu.:-2.081 Class :character FALSE:38 FALSE:10
## Median :-1.887 Mode :character TRUE :10 TRUE :38
## Mean :-1.954
## 3rd Qu.:-1.755
## Max. :-1.717
## chain_step_id
## Min. : 1.00
## 1st Qu.:12.00
## Median :24.00
## Mean :24.27
## 3rd Qu.:36.25
## Max. :48.00
## [1] 48 13
## dv_type dv_type_bin dv_varname id_from id_to beh_difference
## 1 Network 0 self$bipartite_rsienaDV 11 5 0
## 2 Network 0 self$bipartite_rsienaDV 4 4 0
## 3 Network 0 self$bipartite_rsienaDV 12 1 0
## 4 Network 0 self$bipartite_rsienaDV 9 1 0
## 5 Network 0 self$bipartite_rsienaDV 6 3 0
## 6 Network 0 self$bipartite_rsienaDV 3 7 0
## 7 Network 0 self$bipartite_rsienaDV 9 4 0
## 8 Network 0 self$bipartite_rsienaDV 9 4 0
## 9 Network 0 self$bipartite_rsienaDV 1 2 0
## 10 Network 0 self$bipartite_rsienaDV 5 3 0
## 11 Network 0 self$bipartite_rsienaDV 2 6 0
## 12 Network 0 self$bipartite_rsienaDV 4 3 0
## 13 Network 0 self$bipartite_rsienaDV 8 7 0
## 14 Network 0 self$bipartite_rsienaDV 4 2 0
## 15 Network 0 self$bipartite_rsienaDV 6 5 0
## 16 Network 0 self$bipartite_rsienaDV 6 2 0
## 17 Network 0 self$bipartite_rsienaDV 4 7 0
## 18 Network 0 self$bipartite_rsienaDV 12 2 0
## 19 Network 0 self$bipartite_rsienaDV 12 5 0
## 20 Network 0 self$bipartite_rsienaDV 5 3 0
## 21 Network 0 self$bipartite_rsienaDV 8 7 0
## 22 Network 0 self$bipartite_rsienaDV 7 2 0
## 23 Network 0 self$bipartite_rsienaDV 1 1 0
## 24 Network 0 self$bipartite_rsienaDV 10 4 0
## 25 Network 0 self$bipartite_rsienaDV 1 7 0
## 26 Network 0 self$bipartite_rsienaDV 1 4 0
## 27 Network 0 self$bipartite_rsienaDV 7 5 0
## 28 Network 0 self$bipartite_rsienaDV 2 3 0
## 29 Network 0 self$bipartite_rsienaDV 8 7 0
## 30 Network 0 self$bipartite_rsienaDV 7 7 0
## 31 Network 0 self$bipartite_rsienaDV 11 2 0
## 32 Network 0 self$bipartite_rsienaDV 10 3 0
## 33 Network 0 self$bipartite_rsienaDV 11 7 0
## 34 Network 0 self$bipartite_rsienaDV 1 2 0
## 35 Network 0 self$bipartite_rsienaDV 7 1 0
## 36 Network 0 self$bipartite_rsienaDV 9 6 0
## 37 Network 0 self$bipartite_rsienaDV 10 1 0
## 38 Network 0 self$bipartite_rsienaDV 10 5 0
## 39 Network 0 self$bipartite_rsienaDV 3 1 0
## 40 Network 0 self$bipartite_rsienaDV 7 5 0
## 41 Network 0 self$bipartite_rsienaDV 12 7 0
## 42 Network 0 self$bipartite_rsienaDV 7 6 0
## 43 Network 0 self$bipartite_rsienaDV 3 5 0
## 44 Network 0 self$bipartite_rsienaDV 6 6 0
## 45 Network 0 self$bipartite_rsienaDV 9 4 0
## 46 Network 0 self$bipartite_rsienaDV 6 7 0
## 47 Network 0 self$bipartite_rsienaDV 11 3 0
## 48 Network 0 self$bipartite_rsienaDV 5 5 0
## reciprocal_rate LogOptionSetProb LogChoiceProb diagonal stability tie_change
## 1 0.08333333 -2.484907 -2.160006 FALSE FALSE TRUE
## 2 0.08333333 -2.484907 -1.744893 FALSE FALSE TRUE
## 3 0.08333333 -2.484907 -1.882250 FALSE FALSE TRUE
## 4 0.08333333 -2.484907 -1.882250 FALSE FALSE TRUE
## 5 0.08333333 -2.484907 -1.842172 FALSE FALSE TRUE
## 6 0.08333333 -2.484907 -2.058715 FALSE TRUE FALSE
## 7 0.08333333 -2.484907 -1.717030 FALSE FALSE TRUE
## 8 0.08333333 -2.484907 -1.717030 FALSE FALSE TRUE
## 9 0.08333333 -2.484907 -1.755244 FALSE FALSE TRUE
## 10 0.08333333 -2.484907 -1.855504 FALSE FALSE TRUE
## 11 0.08333333 -2.484907 -2.450115 FALSE FALSE TRUE
## 12 0.08333333 -2.484907 -1.870035 FALSE FALSE TRUE
## 13 0.08333333 -2.484907 -1.972047 FALSE TRUE FALSE
## 14 0.08333333 -2.484907 -1.755244 FALSE FALSE TRUE
## 15 0.08333333 -2.484907 -2.146673 FALSE FALSE TRUE
## 16 0.08333333 -2.484907 -1.727381 FALSE FALSE TRUE
## 17 0.08333333 -2.484907 -1.886578 FALSE TRUE FALSE
## 18 0.08333333 -2.484907 -1.727381 FALSE FALSE TRUE
## 19 0.08333333 -2.484907 -2.146673 FALSE FALSE TRUE
## 20 0.08333333 -2.484907 -1.855504 FALSE FALSE TRUE
## 21 0.08333333 -2.484907 -1.972047 FALSE TRUE FALSE
## 22 0.08333333 -2.484907 -1.755244 FALSE FALSE TRUE
## 23 0.08333333 -2.484907 -1.910114 FALSE FALSE TRUE
## 24 0.08333333 -2.484907 -1.744893 FALSE FALSE TRUE
## 25 0.08333333 -2.484907 -1.886578 FALSE TRUE FALSE
## 26 0.08333333 -2.484907 -1.744893 FALSE FALSE TRUE
## 27 0.08333333 -2.484907 -2.174536 FALSE FALSE TRUE
## 28 0.08333333 -2.484907 -1.855504 FALSE FALSE TRUE
## 29 0.08333333 -2.484907 -1.972047 FALSE TRUE FALSE
## 30 0.08333333 -2.484907 -1.886578 FALSE TRUE FALSE
## 31 0.08333333 -2.484907 -1.740714 FALSE FALSE TRUE
## 32 0.08333333 -2.484907 -1.870035 FALSE FALSE TRUE
## 33 0.08333333 -2.484907 -1.972047 FALSE TRUE FALSE
## 34 0.08333333 -2.484907 -1.755244 FALSE FALSE TRUE
## 35 0.08333333 -2.484907 -1.910114 FALSE FALSE TRUE
## 36 0.08333333 -2.484907 -2.436783 FALSE FALSE TRUE
## 37 0.08333333 -2.484907 -1.910114 FALSE FALSE TRUE
## 38 0.08333333 -2.484907 -2.174536 FALSE FALSE TRUE
## 39 0.08333333 -2.484907 -1.882250 FALSE FALSE TRUE
## 40 0.08333333 -2.484907 -2.174536 FALSE FALSE TRUE
## 41 0.08333333 -2.484907 -2.058715 FALSE TRUE FALSE
## 42 0.08333333 -2.484907 -2.464646 FALSE FALSE TRUE
## 43 0.08333333 -2.484907 -2.146673 FALSE FALSE TRUE
## 44 0.08333333 -2.484907 -2.436783 FALSE FALSE TRUE
## 45 0.08333333 -2.484907 -1.717030 FALSE FALSE TRUE
## 46 0.08333333 -2.484907 -2.058715 FALSE TRUE FALSE
## 47 0.08333333 -2.484907 -1.855504 FALSE FALSE TRUE
## 48 0.08333333 -2.484907 -2.160006 FALSE FALSE TRUE
## chain_step_id
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 5
## 7 7
## 8 8
## 9 9
## 10 10
## 11 11
## 12 12
## 13 12
## 14 14
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## 17 16
## 18 18
## 19 19
## 20 20
## 21 20
## 22 22
## 23 23
## 24 24
## 25 24
## 26 26
## 27 27
## 28 28
## 29 28
## 30 28
## 31 31
## 32 32
## 33 32
## 34 34
## 35 35
## 36 36
## 37 37
## 38 38
## 39 39
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## 41 40
## 42 42
## 43 43
## 44 44
## 45 45
## 46 45
## 47 47
## 48 48
##
## =====================================================================
## Model 1
## ---------------------------------------------------------------------
## Rate parameter period 1 0.0650 (0.0605)
## outdegree (density) 0.0000 (0.0000)
## indegree - popularity 0.0000 (0.0000)
## outdegree - activity 0.0000 (0.0000)
## self$component_1_coCovar alter 1.0000 (0.0000) ***
## self$strat_1_coCovar ego 0.1000 (0.0000) ***
## ind. pop.^(1/0.1) weighted self$strat_2_coCovar 0.1000 (0.0000) ***
## ---------------------------------------------------------------------
## Iterations 48
## =====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05## Run Rsiena search using variable parameters in theta_matrix
env1$bi_env_arr[,, c(1,dim(env1$bi_env_arr)[3]) ]## , , 1
##
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0
## [5,] 0 0 0 0 1 0
## [6,] 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0
## [8,] 0 0 0 0 0 0
## [9,] 0 0 0 0 0 0
## [10,] 0 0 0 0 0 0
## [11,] 0 0 0 0 1 0
## [12,] 0 0 0 0 0 0
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 0 0 1 0 0
## [2,] 0 0 1 0 0 1
## [3,] 1 0 0 0 1 0
## [4,] 0 1 1 1 0 0
## [5,] 0 0 0 0 0 0
## [6,] 0 1 1 0 1 1
## [7,] 1 1 0 0 0 1
## [8,] 0 0 0 0 0 0
## [9,] 1 0 0 1 0 1
## [10,] 1 0 1 1 1 0
## [11,] 0 1 1 0 1 0
## [12,] 1 1 0 0 1 01.4 Print Bipartite Search Network Snapshots
Snapshopts of the the biparite network and the social and component epistasis interactions are taken at fixed step intervals to show the evolving multidimensional coupled search environment (actors and components)
# snapshot_ids <- round( seq(1, dim(env1$bi_env_arr)[3], length=3 ) )
snapshot_ids <- c(1, 1+ (1:3)*env1$M)
for (i in 1:length(snapshot_ids)) {
step <- snapshot_ids[ i ]
mat <- env1$bi_env_arr[,,step]
env1$plot_bipartite_system_from_mat(mat, step)
}
1.5 Plot Actor Degrees (Component Scope and Common Affiliation Social Ties)
Time series is simulated decision steps.
##
env1$plot_actor_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.6 Plot Component Degrees (Membership/Popularity and Common Actor Epistasis Ties)
##
env1$plot_component_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.7 Plot Utility by Strategy over Time (Simulated Decision Steps)
## [1] 576 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.8 Plot Utility by Actor over Time (Simulated Decision Steps)
## [1] 576 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
1.9 Plot Contributions to Actor Utility colored by Strategy
Time series is simulated decision steps.
##
env1$plot_utility_components(use_contributions=TRUE, loess_span=0.35)## `geom_smooth()` using formula = 'y ~ x'
1.10 Plot Actor Utility Timeseries and Distribution by Strategy
env1$plot_actor_utility_strategy_summary(loess_span=0.5)
1.11 Actor Utility Distribution by Landscape Distance
Number of changes to component ties as distance from counterfactual affiliation configurations
if (env1$N <= 16)
{
######################
##**TODO**
self <- env1
#
config_param_vals <- c(
unlist(sapply(self$config_structure_model$dv_bipartite$effects, function(x) x$parameter)),
unlist(sapply(self$config_structure_model$dv_bipartite$coCovars, function(x) x$parameter)),
unlist(sapply(self$config_structure_model$dv_bipartite$varCovars, function(x) x$parameter))
)
fixed_params <- c(
unlist(sapply(self$config_structure_model$dv_bipartite$effects, function(x) x$fix)),
unlist(sapply(self$config_structure_model$dv_bipartite$coCovars, function(x) x$fix)),
unlist(sapply(self$config_structure_model$dv_bipartite$varCovars, function(x) x$fix))
)
#
#UTILITY
theta <- self$rsiena_model$thetaUsed
## replace fixed theta param with given param values (instead of zero default when effect is fixed)
# if (any(fixed_params)) theta[ fixed_params ] <- config_param_vals[ fixed_params ]
## PLOT ACTOR UTILITY LANDSCAPE
utilist <- list()
statsl <- list()
pltlist <- list()
# step_ids <- round(c(1,8,64,512, self$rsiena_model$n3/2, self$rsiena_model$n3))
acpds <- seq(1, nrow(theta), by= self$M )
step_ids <- c(acpds[1:min(3, length(acpds))])
for (step_id in step_ids) {
# step_id <- 1
cat(sprintf('\nstep %s: actors ', step_id))
bi_env_mat_step <- self$bi_env_arr[, , step_id]
act_counterfacts <- list()
for (i in 1:min(3, self$M)) {
cat(sprintf(' %s ', i))
##**ACTOR i DECISION PERSPECTIVE** 0000000000000000000000000000000000
##**TODO**
## ALL actor-component counterfactual configuations for Actor i (2^N rows by N cols)
iland <- expand.grid(lapply(1:self$N, function(x) 0:1 ))
iland_config_step_row_id <- which(apply(iland, 1, function(x) all(x == bi_env_mat_step[i,]) ))
tmpmat <- bi_env_mat_step
## ifit dimensions [ M, 2^N ]
ifit <- apply(iland, 1, function(x){
tmpmat[i,] <- x ## set counterfactual actor-component configuration
config_fit <- if('matrix' %in% class(theta)) {
self$get_struct_mod_stats_mat_from_bi_mat( tmpmat ) %*% theta[step_id,] ## variable thetas
} else {
self$get_struct_mod_stats_mat_from_bi_mat( tmpmat ) %*% theta ## fixed thetas
}
return( config_fit ) ## compute utility vector
})
# apply
ids.max <- which(ifit == max(ifit), arr.ind = TRUE)
fits.max <- ifit[ ids.max ]
nmax <- length(fits.max)
# ifit
# ids.max[1,]
act_counterfacts[[i]] <- list(
ifit = ifit, #Given all other ties, Actor i's configurations applied to utility func for all actors
ids.max = ids.max,
fits.max = fits.max,
nmax = nmax
)
## distances of each counterfactual configuration
dist_counterfac <- iland - bi_env_mat_step[i, ]
z <- apply( dist_counterfac, 1, function(x) {
c(dist=sum(x!=0),
`drop`=sum(x==-1),
`nochange`=sum(x==0),
`add`=sum(x==1),
counterfac=paste(x, collapse = '|'),
start=paste(bi_env_mat_step[i, ], collapse = '|')
)
})
# z <- z[,order(z['d',], decreasing = F)]
##**TODO**
##**INFORMATION LOSS**
## This uses only ego's own counterfactual fits (landscape)
## but the corresponding other actor's affected fits (landscapes are not currently used )
z <- rbind(z, utility_ego=ifit[ i , ] )
z <- rbind(z, utility_alter_mean= colMeans(ifit[ -i , ], na.rm = T) )
z <- rbind(z, utility_alter_sd = apply(ifit[ -i , ], 2, function(x) sd(x, na.rm = T)) )
wdf <- as.data.frame( t(z) )
actfit_long <- wdf %>%
mutate(
dist = as.numeric(dist),
drop = as.numeric(drop),
nochange = as.numeric(nochange),
add = as.numeric(add) ,
utility_ego=as.numeric(utility_ego),
utility_alter_mean = as.numeric(utility_alter_mean),
utility_alter_sd = as.numeric(utility_alter_sd)
) %>%
pivot_longer(cols = c( drop:add, utility_ego:utility_alter_sd ))
plt <- actfit_long %>% ggplot(aes(x=value))+
# geom_density() +
geom_histogram() +
facet_grid(dist ~ name, scales='free_x') + theme_bw() +
ggtitle(sprintf('Utility Transition Paths: Actor %s, Step %s', i, step_id))
pltlist[[ length(pltlist)+1 ]] <- plt
# z <- apply(iland, 1, function(x) {
# x <- plyr::count( x - bi_env_mat_step[i, ])
# cnts <- merge(x$freq, data.frame(x=c())
# names(cnts) <- x$x
# return(cnts)
# }, simplify = T)
#
# stringdist::stringdist( , , method='hamming')
step_actor_key <- sprintf('%s|%s',step_id,i)
statsl[[step_actor_key]] <- list(
actfit_long = actfit_long,
act_counterfacts = act_counterfacts,
plt = plt,
actor_id=i, chain_step_id = step_id,
strategy=as.factor(self$strat_1_coCovar[i])
)
actor_strats <- env1$get_actor_strategies()
utilist[[step_actor_key]] <- actfit_long %>%
filter(name %in% c('utility_ego','utility_alter_mean')) %>%
mutate(actor_id=i, chain_step_id = step_id, strategy=as.factor(actor_strats[i]))
} ##/end actor loop
# # Arrange plots across multiple pages
# multi_page_plot <- marrangeGrob(pltlist, nrow = 1, ncol = 1)
#
# # Save to a PDF
# ggsave("multi_page_plots.pdf", multi_page_plot, width = 8, height = 8)
} ##/end step loop
#
utildf <- data.table::rbindlist(utilist, use.names = T, idcol = 'step_actor_key')
##--------------------------------------------------------------
## 4. Actor Utility Transition Paths
##--------------------------------------------------------------
# Arrange plots across multiple pages
plt_act_paths <- marrangeGrob(pltlist, nrow = 1, ncol = 1)
plt_act_paths
# # Save to a PDF
# actor_path_plotfile <- sprintf('%s_actor_pd_dist2peaks.pdf', self$UUID)
# plot_dir <- getwd()
# ggsave(filename = file.path(plot_dir, actor_path_plotfile), plt_act_paths,
# width = 8, height = 8)
} ##
## step 1: actors 1 2 3
## step 13: actors 1 2 3
## step 25: actors 1 2 3## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
2. Environmental WITH Endogenous Noise
Structural Effects in range [-1, 0.4]
2.1 Environmental Configuration
Reuse existing environ_config
2.2 Structrual Model
SAOM Objective Function serves as the stochastic actor’s utility function for strategic search.
strategies <- list(
egoX = c(-1, 0, 1),
inPopX = c( 1, 0, -1)
)
## 2.b. Component Payoffs vector
set.seed(12345)
component_payoffs <- runif(environ_params$N, min = 0, max = 1)
## 2. Strategies sets the objective function as a linear combination of network stats across DVs
#
actor_strats_list <- lapply(strategies, function(strat) rep(strat, environ_params$M/length(strat)) )
#
structure_model <- list(
dv_bipartite = list(
name = 'self$bipartite_rsienaDV',
effects = list( ##**STRUCTURAL EFFECTS -- dyadic/network endogeneity sources**
list(effect='density', parameter= -1, fix=T, dv_name=DV_NAME), ##interaction1 = NULL
list(effect='inPop', parameter= .1, fix=T, dv_name=DV_NAME), #interaction1 = NUL
list(effect='outAct', parameter= .1, fix=T, dv_name=DV_NAME)
),
## COVARIATE EFFECTS
coCovars = list(
##** COMPONENTS : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='altX', parameter= 1, fix=T,dv_name=DV_NAME,
interaction1='self$component_1_coCovar', x = component_payoffs
),
##** STRATEGIES : MONADIC CONSTANT COVARIATE EFFECTS **##
list(effect='egoX', parameter= .1, fix=T,dv_name=DV_NAME,
interaction1='self$strat_1_coCovar', x = actor_strats_list[[1]]
), #interaction1 = NULL
list(effect='inPopX', parameter= .1, fix=T,dv_name=DV_NAME,
interaction1='self$strat_2_coCovar', x = actor_strats_list[[2]]
)
),
varCovars = list() ##**MONADIC TIME-VARYING COVARIATE EFFECTS -- DYNAMIC STRATEGY PROGRAMS**
)
)2.3 Run RSiena Search Process
## Run Rsiena search using variable parameters in theta_matrix
env1$search_rsiena(
structure_model,
iterations = env1$M * 4,
digits = 4,
run_seed = 12345
)##
##
## theta_matrix :
##
## density inPop outAct altX egoX inPopX
## [1,] -1 0.1 0.1 1 0.1 0.1
## [2,] -1 0.1 0.1 1 0.1 0.1
## [3,] -1 0.1 0.1 1 0.1 0.1
## [4,] -1 0.1 0.1 1 0.1 0.1
## [5,] -1 0.1 0.1 1 0.1 0.1
## [6,] -1 0.1 0.1 1 0.1 0.1
## [7,] -1 0.1 0.1 1 0.1 0.1
## [8,] -1 0.1 0.1 1 0.1 0.1
## [9,] -1 0.1 0.1 1 0.1 0.1
## [10,] -1 0.1 0.1 1 0.1 0.1
## [11,] -1 0.1 0.1 1 0.1 0.1
## [12,] -1 0.1 0.1 1 0.1 0.1
## [13,] -1 0.1 0.1 1 0.1 0.1
## [14,] -1 0.1 0.1 1 0.1 0.1
## [15,] -1 0.1 0.1 1 0.1 0.1
## [16,] -1 0.1 0.1 1 0.1 0.1
## [17,] -1 0.1 0.1 1 0.1 0.1
## [18,] -1 0.1 0.1 1 0.1 0.1
## [19,] -1 0.1 0.1 1 0.1 0.1
## [20,] -1 0.1 0.1 1 0.1 0.1
## [21,] -1 0.1 0.1 1 0.1 0.1
## [22,] -1 0.1 0.1 1 0.1 0.1
## [23,] -1 0.1 0.1 1 0.1 0.1
## [24,] -1 0.1 0.1 1 0.1 0.1
## [25,] -1 0.1 0.1 1 0.1 0.1
## [26,] -1 0.1 0.1 1 0.1 0.1
## [27,] -1 0.1 0.1 1 0.1 0.1
## [28,] -1 0.1 0.1 1 0.1 0.1
## [29,] -1 0.1 0.1 1 0.1 0.1
## [30,] -1 0.1 0.1 1 0.1 0.1
## [31,] -1 0.1 0.1 1 0.1 0.1
## [32,] -1 0.1 0.1 1 0.1 0.1
## [33,] -1 0.1 0.1 1 0.1 0.1
## [34,] -1 0.1 0.1 1 0.1 0.1
## [35,] -1 0.1 0.1 1 0.1 0.1
## [36,] -1 0.1 0.1 1 0.1 0.1
## [37,] -1 0.1 0.1 1 0.1 0.1
## [38,] -1 0.1 0.1 1 0.1 0.1
## [39,] -1 0.1 0.1 1 0.1 0.1
## [40,] -1 0.1 0.1 1 0.1 0.1
## [41,] -1 0.1 0.1 1 0.1 0.1
## [42,] -1 0.1 0.1 1 0.1 0.1
## [43,] -1 0.1 0.1 1 0.1 0.1
## [44,] -1 0.1 0.1 1 0.1 0.1
## [45,] -1 0.1 0.1 1 0.1 0.1
## [46,] -1 0.1 0.1 1 0.1 0.1
## [47,] -1 0.1 0.1 1 0.1 0.1
## [48,] -1 0.1 0.1 1 0.1 0.1
##
##
## self$rsiena_data :
##
## Dependent variables: self$bipartite_rsienaDV
## Number of observations: 2
##
## Nodesets ACTORS COMPONENTS
## Number of nodes 12 6
##
## Dependent variable self$bipartite_rsienaDV
## Type bipartite
## Observations 2
## First nodeset ACTORS
## Second nodeset COMPONENTS
## Densities 0.014 0.014
##
## Constant covariates: self$component_1_coCovar, self$strat_1_coCovar, self$strat_2_coCovar
##
## structural effects i=1, j=1
## $effect
## [1] "density"
##
## $parameter
## [1] -1
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 outdegree (density) TRUE TRUE FALSE 0 -1
##
## structural effects i=1, j=2
## $effect
## [1] "inPop"
##
## $parameter
## [1] 0.1
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 indegree - popularity TRUE TRUE FALSE 0 0.1
##
## structural effects i=1, j=3
## $effect
## [1] "outAct"
##
## $parameter
## [1] 0.1
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 outdegree - activity TRUE TRUE FALSE 0 0.1
##
## coCovars i=1, j=1
## $effect
## [1] "altX"
##
## $parameter
## [1] 1
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $interaction1
## [1] "self$component_1_coCovar"
##
## $x
## [1] 0.7209039 0.8757732 0.7609823 0.8861246 0.4564810 0.1663718
##
## effectName include fix test initialValue parm
## 1 self$component_1_coCovar alter TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$component_1_coCovar alter TRUE TRUE FALSE 0 1
##
## coCovars i=1, j=2
## $effect
## [1] "egoX"
##
## $parameter
## [1] 0.1
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $interaction1
## [1] "self$strat_1_coCovar"
##
## $x
## [1] -1 0 1 -1 0 1 -1 0 1 -1 0 1
##
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0
## effectName include fix test initialValue parm
## 1 self$strat_1_coCovar ego TRUE TRUE FALSE 0 0.1
##
## coCovars i=1, j=3
## $effect
## [1] "inPopX"
##
## $parameter
## [1] 0.1
##
## $fix
## [1] TRUE
##
## $dv_name
## [1] "self$bipartite_rsienaDV"
##
## $interaction1
## [1] "self$strat_2_coCovar"
##
## $x
## [1] 1 0 -1 1 0 -1 1 0 -1 1 0 -1
##
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 1
## effectName include fix test initialValue
## 1 ind. pop.^(1/#) weighted self$strat_2_coCovar TRUE TRUE FALSE 0
## parm
## 1 0.1
## If you use this algorithm object, siena07 will create/use an output file C:/Users/sdr8y/OneDrive - University of Missouri/Research/Search_networks/SaoMNK/R/_test_tutorial_nb__173813296720.txt .
##
## Start phase 0
## theta: 0 0 0 0 0 0
##
## Start phase 3
## Parameter values used for simulations
##
## Mean Standard
## value Deviation
##
## Rate parameters:
## 0 Rate parameter NA ( NA )
##
## Other parameters:
## 1. eval outdegree (density) -1.0 ( 0 )
## 2. eval indegree - popularity 0.1 ( 0 )
## 3. eval outdegree - activity 0.1 ( 0 )
## 4. eval self$component_1_coCovar alter 1.0 ( 0 )
## 5. eval self$strat_1_coCovar ego 0.1 ( 0 )
## 6. eval ind. pop.^(1/0.1) weighted self$strat_2_coCovar 0.1 ( 0 )
##
## Simulated means and standard deviations
## 1. Number of ties 1.604 0.536
## 2. Sum of squared indegrees 1.729 0.792
## 3. Sum of squared outdegrees 1.688 0.719
## 4. Sum of indegrees x self$component_1_coCovar -0.154 0.195
## 5. Sum of outdegrees x self$strat_1_coCovar -0.062 0.727
## 6. indegree pop.^(1/0.1) weighted self$strat_2_coCovar 0.042 0.849
##
##
## Simulated statistics are in x$sf
## and simulated dependent variables in x$sims, where x is the created object.
##
## Total of 48 iteration steps.
##
## Covariance matrix of estimates (correlations below diagonal)
##
## 0 0 0 0 0 0
## NaN 0 0 0 0 0
## NaN NaN 0 0 0 0
## NaN NaN NaN 0 0 0
## NaN NaN NaN NaN 0 0
## NaN NaN NaN NaN NaN 0
##
## Derivative matrix of expected statistics X by parameters:
##
## 0.270 0.313 0.357 0.014 -0.032 0.026
## 0.293 0.452 0.384 0.002 -0.008 -0.017
## 0.318 0.357 0.496 0.021 -0.030 0.025
## 0.007 -0.020 0.018 0.036 -0.021 0.025
## 0.001 0.016 0.005 -0.004 0.135 -0.152
## 0.000 0.000 0.000 0.000 0.000 0.000
##
## Covariance matrix of X (correlations below diagonal):
##
## 0.287 0.337 0.320 0.006 -0.025 0.017
## 0.795 0.627 0.360 -0.023 0.025 -0.074
## 0.832 0.633 0.517 0.023 -0.020 0.013
## 0.054 -0.147 0.167 0.038 -0.020 0.025
## -0.065 0.044 -0.038 -0.143 0.528 -0.593
## 0.037 -0.109 0.022 0.151 -0.961 0.722
##
##
##
## Simulated Decision Chain Summary:
##
## dv_type dv_type_bin dv_varname id_from
## Length:48 Min. :0 Length:48 Min. : 1.000
## Class :character 1st Qu.:0 Class :character 1st Qu.: 4.000
## Mode :character Median :0 Mode :character Median : 7.000
## Mean :0 Mean : 6.708
## 3rd Qu.:0 3rd Qu.: 9.250
## Max. :0 Max. :12.000
##
## id_to beh_difference reciprocal_rate LogOptionSetProb
## Min. :1.000 Min. :0 Min. :0.08333 Min. :-2.485
## 1st Qu.:2.750 1st Qu.:0 1st Qu.:0.08333 1st Qu.:-2.485
## Median :5.000 Median :0 Median :0.08333 Median :-2.485
## Mean :4.583 Mean :0 Mean :0.08333 Mean :-2.485
## 3rd Qu.:7.000 3rd Qu.:0 3rd Qu.:0.08333 3rd Qu.:-2.485
## Max. :7.000 Max. :0 Max. :0.08333 Max. :-2.485
##
## LogChoiceProb diagonal stability tie_change
## Min. :-2.645 Length:48 Mode :logical Mode :logical
## 1st Qu.:-2.050 Class :character FALSE:31 FALSE:17
## Median :-1.930 Mode :character TRUE :17 TRUE :31
## Mean :-1.813
## 3rd Qu.:-1.396
## Max. :-0.903
##
## chain_step_id
## Min. : 2.00
## 1st Qu.:13.00
## Median :24.00
## Mean :24.51
## 3rd Qu.:36.00
## Max. :48.00
## NA's :1
## [1] 48 13
## dv_type dv_type_bin dv_varname id_from id_to beh_difference
## 1 Network 0 self$bipartite_rsienaDV 11 7 0
## 2 Network 0 self$bipartite_rsienaDV 4 4 0
## 3 Network 0 self$bipartite_rsienaDV 12 1 0
## 4 Network 0 self$bipartite_rsienaDV 9 1 0
## 5 Network 0 self$bipartite_rsienaDV 6 3 0
## 6 Network 0 self$bipartite_rsienaDV 3 7 0
## 7 Network 0 self$bipartite_rsienaDV 9 5 0
## 8 Network 0 self$bipartite_rsienaDV 9 4 0
## 9 Network 0 self$bipartite_rsienaDV 1 2 0
## 10 Network 0 self$bipartite_rsienaDV 5 4 0
## 11 Network 0 self$bipartite_rsienaDV 2 7 0
## 12 Network 0 self$bipartite_rsienaDV 4 3 0
## 13 Network 0 self$bipartite_rsienaDV 8 7 0
## 14 Network 0 self$bipartite_rsienaDV 4 2 0
## 15 Network 0 self$bipartite_rsienaDV 6 6 0
## 16 Network 0 self$bipartite_rsienaDV 6 2 0
## 17 Network 0 self$bipartite_rsienaDV 4 7 0
## 18 Network 0 self$bipartite_rsienaDV 12 2 0
## 19 Network 0 self$bipartite_rsienaDV 12 6 0
## 20 Network 0 self$bipartite_rsienaDV 5 4 0
## 21 Network 0 self$bipartite_rsienaDV 8 7 0
## 22 Network 0 self$bipartite_rsienaDV 7 2 0
## 23 Network 0 self$bipartite_rsienaDV 1 2 0
## 24 Network 0 self$bipartite_rsienaDV 10 4 0
## 25 Network 0 self$bipartite_rsienaDV 1 7 0
## 26 Network 0 self$bipartite_rsienaDV 1 5 0
## 27 Network 0 self$bipartite_rsienaDV 7 7 0
## 28 Network 0 self$bipartite_rsienaDV 2 3 0
## 29 Network 0 self$bipartite_rsienaDV 8 7 0
## 30 Network 0 self$bipartite_rsienaDV 7 7 0
## 31 Network 0 self$bipartite_rsienaDV 11 2 0
## 32 Network 0 self$bipartite_rsienaDV 10 3 0
## 33 Network 0 self$bipartite_rsienaDV 11 7 0
## 34 Network 0 self$bipartite_rsienaDV 1 3 0
## 35 Network 0 self$bipartite_rsienaDV 7 1 0
## 36 Network 0 self$bipartite_rsienaDV 9 7 0
## 37 Network 0 self$bipartite_rsienaDV 10 1 0
## 38 Network 0 self$bipartite_rsienaDV 10 6 0
## 39 Network 0 self$bipartite_rsienaDV 3 1 0
## 40 Network 0 self$bipartite_rsienaDV 7 6 0
## 41 Network 0 self$bipartite_rsienaDV 12 7 0
## 42 Network 0 self$bipartite_rsienaDV 7 7 0
## 43 Network 0 self$bipartite_rsienaDV 3 7 0
## 44 Network 0 self$bipartite_rsienaDV 6 7 0
## 45 Network 0 self$bipartite_rsienaDV 9 5 0
## 46 Network 0 self$bipartite_rsienaDV 6 7 0
## 47 Network 0 self$bipartite_rsienaDV 11 3 0
## 48 Network 0 self$bipartite_rsienaDV 5 5 0
## reciprocal_rate LogOptionSetProb LogChoiceProb diagonal stability tie_change
## 1 0.08333333 -2.484907 -1.3398382 FALSE TRUE FALSE
## 2 0.08333333 -2.484907 -1.9253236 FALSE FALSE TRUE
## 3 0.08333333 -2.484907 -2.0381360 FALSE FALSE TRUE
## 4 0.08333333 -2.484907 -2.0381360 FALSE FALSE TRUE
## 5 0.08333333 -2.484907 -1.9980576 FALSE FALSE TRUE
## 6 0.08333333 -2.484907 -1.4146004 FALSE TRUE FALSE
## 7 0.08333333 -2.484907 -2.2025589 FALSE FALSE TRUE
## 8 0.08333333 -2.484907 -1.8729153 FALSE FALSE TRUE
## 9 0.08333333 -2.484907 -1.9356750 FALSE FALSE TRUE
## 10 0.08333333 -2.484907 -2.2492524 FALSE FALSE TRUE
## 11 0.08333333 -2.484907 -1.3398382 FALSE TRUE FALSE
## 12 0.08333333 -2.484907 -2.0504658 FALSE FALSE TRUE
## 13 0.08333333 -2.484907 -1.3398382 FALSE TRUE FALSE
## 14 0.08333333 -2.484907 -1.9356750 FALSE FALSE TRUE
## 15 0.08333333 -2.484907 -2.5926681 FALSE FALSE TRUE
## 16 0.08333333 -2.484907 -1.8832667 FALSE FALSE TRUE
## 17 0.08333333 -2.484907 -1.2670087 FALSE TRUE FALSE
## 18 0.08333333 -2.484907 -1.8832667 FALSE FALSE TRUE
## 19 0.08333333 -2.484907 -2.5926681 FALSE FALSE TRUE
## 20 0.08333333 -2.484907 -2.2492524 FALSE FALSE TRUE
## 21 0.08333333 -2.484907 -1.3398382 FALSE TRUE FALSE
## 22 0.08333333 -2.484907 -1.9356750 FALSE FALSE TRUE
## 23 0.08333333 -2.484907 -1.9356750 FALSE FALSE TRUE
## 24 0.08333333 -2.484907 -1.9253236 FALSE FALSE TRUE
## 25 0.08333333 -2.484907 -1.2670087 FALSE TRUE FALSE
## 26 0.08333333 -2.484907 -2.2549672 FALSE FALSE TRUE
## 27 0.08333333 -2.484907 -1.2670087 FALSE TRUE FALSE
## 28 0.08333333 -2.484907 -2.0232953 FALSE FALSE TRUE
## 29 0.08333333 -2.484907 -1.3398382 FALSE TRUE FALSE
## 30 0.08333333 -2.484907 -1.2670087 FALSE TRUE FALSE
## 31 0.08333333 -2.484907 -1.9085045 FALSE FALSE TRUE
## 32 0.08333333 -2.484907 -2.0504658 FALSE FALSE TRUE
## 33 0.08333333 -2.484907 -1.3398382 FALSE TRUE FALSE
## 34 0.08333333 -2.484907 -2.0504658 FALSE FALSE TRUE
## 35 0.08333333 -2.484907 -2.0905443 FALSE FALSE TRUE
## 36 0.08333333 -2.484907 -1.4146004 FALSE TRUE FALSE
## 37 0.08333333 -2.484907 -2.0905443 FALSE FALSE TRUE
## 38 0.08333333 -2.484907 -2.6450764 FALSE FALSE TRUE
## 39 0.08333333 -2.484907 -2.0381360 FALSE FALSE TRUE
## 40 0.08333333 -2.484907 -2.6450764 FALSE FALSE TRUE
## 41 0.08333333 -2.484907 -1.4146004 FALSE TRUE FALSE
## 42 0.08333333 -2.484907 -1.2670087 FALSE TRUE FALSE
## 43 0.08333333 -2.484907 -1.4146004 FALSE TRUE FALSE
## 44 0.08333333 -2.484907 -1.4146004 FALSE TRUE FALSE
## 45 0.08333333 -2.484907 -2.2025589 FALSE FALSE TRUE
## 46 0.08333333 -2.484907 -1.4146004 FALSE TRUE FALSE
## 47 0.08333333 -2.484907 -2.0232953 FALSE FALSE TRUE
## 48 0.08333333 -2.484907 -0.9029791 FALSE FALSE TRUE
## chain_step_id
## 1 NA
## 2 2
## 3 3
## 4 4
## 5 5
## 6 5
## 7 7
## 8 8
## 9 9
## 10 10
## 11 10
## 12 12
## 13 12
## 14 14
## 15 15
## 16 16
## 17 16
## 18 18
## 19 19
## 20 20
## 21 20
## 22 22
## 23 23
## 24 24
## 25 24
## 26 26
## 27 26
## 28 28
## 29 28
## 30 28
## 31 31
## 32 32
## 33 32
## 34 34
## 35 35
## 36 35
## 37 37
## 38 38
## 39 39
## 40 40
## 41 40
## 42 40
## 43 40
## 44 40
## 45 45
## 46 45
## 47 47
## 48 48
##
## =====================================================================
## Model 1
## ---------------------------------------------------------------------
## Rate parameter period 1 0.0650 (0.0605)
## outdegree (density) -1.0000 (0.0000) ***
## indegree - popularity 0.1000 (0.0000) ***
## outdegree - activity 0.1000 (0.0000) ***
## self$component_1_coCovar alter 1.0000 (0.0000) ***
## self$strat_1_coCovar ego 0.1000 (0.0000) ***
## ind. pop.^(1/0.1) weighted self$strat_2_coCovar 0.1000 (0.0000) ***
## ---------------------------------------------------------------------
## Iterations 48
## =====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.052.4 Print Bipartite Search Network Snapshots
Snapshopts of the the biparite network and the social and component epistasis interactions are taken at fixed step intervals to show the evolving multidimensional coupled search environment (actors and components)
# snapshot_ids <- round( seq(1, dim(env1$bi_env_arr)[3], length=3 ) )
snapshot_ids <- c(1, 1+ (1:3)*env1$M)
for (i in 1:length(snapshot_ids)) {
step <- snapshot_ids[ i ]
mat <- env1$bi_env_arr[,,step]
env1$plot_bipartite_system_from_mat(mat, step)
}
2.5 Plot Actor Degrees (Component Scope and Common Affiliation Social Ties)
Time series is simulated decision steps.
##
env1$plot_actor_degrees(loess_span = 0.25)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
2.6 Plot Component Degrees (Membership/Popularity and Common Actor Epistasis Ties)
##
env1$plot_component_degrees(loess_span = 0.35)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
2.7 Plot Utility by Strategy over Time (Simulated Decision Steps)
## [1] 576 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
2.8 Plot Utility by Actor over Time (Simulated Decision Steps)
## [1] 576 5## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
2.9 Plot Contributions to Actor Utility colored by Strategy
Time series is simulated decision steps.
##
env1$plot_utility_components(use_contributions=T, loess_span=0.35)## `geom_smooth()` using formula = 'y ~ x'
2.10 Plot Actor Utility Timeseries and Distribution by Strategy
env1$plot_actor_utility_strategy_summary()
2.11 Actor Utility Distribution by Landscape Distance
Number of changes to component ties as distance from counterfactual affiliation configurations
if (env1$N <= 16)
{
######################
##**TODO**
self <- env1
#
config_param_vals <- c(
unlist(sapply(self$config_structure_model$dv_bipartite$effects, function(x) x$parameter)),
unlist(sapply(self$config_structure_model$dv_bipartite$coCovars, function(x) x$parameter)),
unlist(sapply(self$config_structure_model$dv_bipartite$varCovars, function(x) x$parameter))
)
fixed_params <- c(
unlist(sapply(self$config_structure_model$dv_bipartite$effects, function(x) x$fix)),
unlist(sapply(self$config_structure_model$dv_bipartite$coCovars, function(x) x$fix)),
unlist(sapply(self$config_structure_model$dv_bipartite$varCovars, function(x) x$fix))
)
#
#UTILITY
theta <- self$rsiena_model$thetaUsed
## replace fixed theta param with given param values (instead of zero default when effect is fixed)
# if (any(fixed_params)) theta[ fixed_params ] <- config_param_vals[ fixed_params ]
## PLOT ACTOR UTILITY LANDSCAPE
utilist <- list()
statsl <- list()
pltlist <- list()
# step_ids <- round(c(1,8,64,512, self$rsiena_model$n3/2, self$rsiena_model$n3))
# step_ids <- round(c(1,8,64,512, self$rsiena_model$n3/2, self$rsiena_model$n3))
acpds <- seq(1, nrow(theta), by= self$M )
step_ids <- c(acpds[1:min(3, length(acpds))])
for (step_id in step_ids) {
# step_id <- 1
cat(sprintf('\nstep %s: actors ', step_id))
bi_env_mat_step <- self$bi_env_arr[, , step_id]
act_counterfacts <- list()
for (i in 1:min(3, self$M)) {
cat(sprintf(' %s ', i))
##**ACTOR i DECISION PERSPECTIVE** 0000000000000000000000000000000000
##**TODO**
## ALL actor-component counterfactual configuations for Actor i (2^N rows by N cols)
iland <- expand.grid(lapply(1:self$N, function(x) 0:1 ))
iland_config_step_row_id <- which(apply(iland, 1, function(x) all(x == bi_env_mat_step[i,]) ))
tmpmat <- bi_env_mat_step
## ifit dimensions [ M, 2^N ]
ifit <- apply(iland, 1, function(x){
tmpmat[i,] <- x ## set counterfactual actor-component configuration
config_fit <- if('matrix' %in% class(theta)) {
self$get_struct_mod_stats_mat_from_bi_mat( tmpmat ) %*% theta[step_id,]
} else {
self$get_struct_mod_stats_mat_from_bi_mat( tmpmat ) %*% theta
}
return( config_fit ) ## compute utility vector
})
# apply
ids.max <- which(ifit == max(ifit), arr.ind = TRUE)
fits.max <- ifit[ ids.max ]
nmax <- length(fits.max)
# ifit
# ids.max[1,]
act_counterfacts[[i]] <- list(
ifit = ifit, #Given all other ties, Actor i's configurations applied to utility func for all actors
ids.max = ids.max,
fits.max = fits.max,
nmax = nmax
)
## distances of each counterfactual configuration
dist_counterfac <- iland - bi_env_mat_step[i, ]
z <- apply( dist_counterfac, 1, function(x) {
c(dist=sum(x!=0),`drop`=sum(x==-1), `nochange`=sum(x==0), `add`=sum(x==1),
counterfac=paste(x, collapse = '|'), start=paste(bi_env_mat_step[i, ], collapse = '|') )
})
# z <- z[,order(z['d',], decreasing = F)]
##**TODO**
##**INFORMATION LOSS**
## This uses only ego's own counterfactual fits (landscape)
## but the corresponding other actor's affected fits (landscapes are not currently used )
z <- rbind(z, utility_ego=ifit[ i , ] )
z <- rbind(z, utility_alter_mean= colMeans(ifit[ -i , ], na.rm = T) )
z <- rbind(z, utility_alter_sd = apply(ifit[ -i , ], 2, function(x) sd(x, na.rm = T)) )
wdf <- as.data.frame( t(z) )
actfit_long <- wdf %>%
mutate(
dist = as.numeric(dist),
drop = as.numeric(drop),
nochange = as.numeric(nochange),
add = as.numeric(add) ,
utility_ego=as.numeric(utility_ego),
utility_alter_mean = as.numeric(utility_alter_mean),
utility_alter_sd = as.numeric(utility_alter_sd)
) %>%
pivot_longer(cols = c( drop:add, utility_ego:utility_alter_sd ))
plt <- actfit_long %>% ggplot(aes(x=value))+
# geom_density() +
geom_histogram() +
facet_grid(dist ~ name, scales='free_x') + theme_bw() +
ggtitle(sprintf('Utility Transition Paths: Actor %s, Step %s', i, step_id))
pltlist[[ length(pltlist)+1 ]] <- plt
# z <- apply(iland, 1, function(x) {
# x <- plyr::count( x - bi_env_mat_step[i, ])
# cnts <- merge(x$freq, data.frame(x=c())
# names(cnts) <- x$x
# return(cnts)
# }, simplify = T)
#
# stringdist::stringdist( , , method='hamming')
step_actor_key <- sprintf('%s|%s',step_id,i)
statsl[[step_actor_key]] <- list(
actfit_long = actfit_long,
act_counterfacts = act_counterfacts,
plt = plt,
actor_id=i, chain_step_id = step_id,
strategy=as.factor(self$strat_1_coCovar[i])
)
utilist[[step_actor_key]] <- actfit_long %>%
filter(name %in% c('utility_ego','utility_alter_mean')) %>%
mutate(actor_id=i, chain_step_id = step_id, strategy=as.factor(self$strat_1_coCovar[i]))
} ##/end actor loop
# # Arrange plots across multiple pages
# multi_page_plot <- marrangeGrob(pltlist, nrow = 1, ncol = 1)
#
# # Save to a PDF
# ggsave("multi_page_plots.pdf", multi_page_plot, width = 8, height = 8)
} ##/end step loop
#
utildf <- data.table::rbindlist(utilist, use.names = T, idcol = 'step_actor_key')
##--------------------------------------------------------------
## 4. Actor Utility Transition Paths
##--------------------------------------------------------------
# Arrange plots across multiple pages
plt_act_paths <- marrangeGrob(pltlist, nrow = 1, ncol = 1)
plt_act_paths
# # Save to a PDF
# actor_path_plotfile <- sprintf('%s_actor_pd_dist2peaks.pdf', self$UUID)
# plot_dir <- getwd()
# ggsave(filename = file.path(plot_dir, actor_path_plotfile), plt_act_paths,
# width = 8, height = 8)
}##
## step 1: actors 1 2 3
## step 13: actors 1 2 3
## step 25: actors 1 2 3## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
